Zhang Yinan, An Mingqiang
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China; College of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300222, China.
College of Science, Tianjin University of Science and Technology, Tianjin 300222, China.
Comput Math Methods Med. 2016;2016:4345936. doi: 10.1155/2016/4345936. Epub 2016 Aug 29.
Diabetic retinopathy (DR) screening system raises a financial problem. For further reducing DR screening cost, an active learning classifier is proposed in this paper. Our approach identifies retinal images based on features extracted by anatomical part recognition and lesion detection algorithms. Kernel extreme learning machine (KELM) is a rapid classifier for solving classification problems in high dimensional space. Both active learning and ensemble technique elevate performance of KELM when using small training dataset. The committee only proposes necessary manual work to doctor for saving cost. On the publicly available Messidor database, our classifier is trained with 20%-35% of labeled retinal images and comparative classifiers are trained with 80% of labeled retinal images. Results show that our classifier can achieve better classification accuracy than Classification and Regression Tree, radial basis function SVM, Multilayer Perceptron SVM, Linear SVM, and Nearest Neighbor. Empirical experiments suggest that our active learning classifier is efficient for further reducing DR screening cost.
糖尿病视网膜病变（DR）筛查系统引发了一个资金问题。为了进一步降低DR筛查成本，本文提出了一种主动学习分类器。我们的方法基于通过解剖部位识别和病变检测算法提取的特征来识别视网膜图像。核极限学习机（KELM）是一种用于解决高维空间中分类问题的快速分类器。当使用小训练数据集时，主动学习和集成技术都能提高KELM的性能。该委员会仅向医生提出必要的人工工作以节省成本。在公开可用的Messidor数据库上，我们的分类器使用20% - 35%的标记视网膜图像进行训练，而对比分类器使用80%的标记视网膜图像进行训练。结果表明，我们的分类器能够比分类与回归树、径向基函数支持向量机、多层感知器支持向量机、线性支持向量机和最近邻算法实现更好的分类准确率。实证实验表明，我们的主动学习分类器对于进一步降低DR筛查成本是有效的。
